Semantic Information Fusion to Enhance Situational Awareness in Surveillance Scenarios
| Title | Semantic Information Fusion to Enhance Situational Awareness in Surveillance Scenarios |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | MüUller, W., Kuwertz, A., Mühlenberg, D., Sander, J. |
| Conference Name | 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) |
| Publisher | IEEE |
| ISBN Number | 978-1-5090-6064-1 |
| Keywords | aerospace computing, autonomous aerial vehicles, civil protection, Cognition, critical events detection, Data integration, data mining, Databases, enhanced situational awareness, high-level data fusion component, inference mechanisms, information integration, intelligence-surveillance-and-reconnaissance analytics architecture, ISR-AA, knowledge model, logical reasoning, Markov logic network, Markov processes, military applications, military computing, National security, Network reconnaissance, Object oriented modeling, object-oriented methods, object-oriented world model, ontologies (artificial intelligence), Ontology, OOWM, probabilistic information processing, Probabilistic logic, probabilistic reasoning, pubcrawl, reasoning component, Resiliency, security forces, semantic information fusion, sensor data, sensor fusion, situational awareness, situational picture, surveillance, surveillance scenarios, UAS, unmanned aircraft systems, video surveillance |
| Abstract | In recent years, the usage of unmanned aircraft systems (UAS) for security-related purposes has increased, ranging from military applications to different areas of civil protection. The deployment of UAS can support security forces in achieving an enhanced situational awareness. However, in order to provide useful input to a situational picture, sensor data provided by UAS has to be integrated with information about the area and objects of interest from other sources. The aim of this study is to design a high-level data fusion component combining probabilistic information processing with logical and probabilistic reasoning, to support human operators in their situational awareness and improving their capabilities for making efficient and effective decisions. To this end, a fusion component based on the ISR (Intelligence, Surveillance and Reconnaissance) Analytics Architecture (ISR-AA) [1] is presented, incorporating an object-oriented world model (OOWM) for information integration, an expressive knowledge model and a reasoning component for detection of critical events. Approaches for translating the information contained in the OOWM into either an ontology for logical reasoning or a Markov logic network for probabilistic reasoning are presented. |
| URL | http://ieeexplore.ieee.org/document/8170353/ |
| DOI | 10.1109/MFI.2017.8170353 |
| Citation Key | muuller_semantic_2017 |
- inference mechanisms
- Network reconnaissance
- National security
- military computing
- military applications
- Markov processes
- Markov logic network
- logical reasoning
- knowledge model
- ISR-AA
- intelligence-surveillance-and-reconnaissance analytics architecture
- information integration
- Object oriented modeling
- high-level data fusion component
- enhanced situational awareness
- Databases
- Data mining
- Data integration
- critical events detection
- cognition
- civil protection
- autonomous aerial vehicles
- aerospace computing
- Resiliency
- Unmanned Aircraft Systems
- UAS
- surveillance scenarios
- surveillance
- situational picture
- situational awareness
- sensor fusion
- sensor data
- semantic information fusion
- security forces
- video surveillance
- reasoning component
- pubcrawl
- probabilistic reasoning
- Probabilistic logic
- probabilistic information processing
- OOWM
- Ontology
- ontologies (artificial intelligence)
- object-oriented world model
- object-oriented methods